Methodological Evaluation and Time-Series Forecasting for Cost-Effectiveness in Senegalese Manufacturing Systems
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| Format: | Recurso digital |
| Language: | English |
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2007
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| _version_ | 1866901943658479616 |
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| author | Ndiaye, Moussa Diop, Aminata Sarr, Fatou |
| author_facet | Ndiaye, Moussa Diop, Aminata Sarr, Fatou |
| contents | <p>{ "background": "Manufacturing systems in West Africa face persistent challenges in achieving cost-effectiveness, with a recognised need for robust analytical frameworks to support operational decision-making. Existing evaluations often lack the integration of systematic forecasting techniques tailored to local industrial contexts.", "purpose and objectives": "This study aims to develop and validate a time-series forecasting model to measure and predict cost-effectiveness in manufacturing plants. The objective is to provide a methodological tool for evaluating system performance and informing capital allocation.", "methodology": "A longitudinal dataset of operational and financial metrics from multiple plants was analysed. The core methodological innovation is a seasonal autoregressive integrated moving average (SARIMA) model, specified as $\\phi(B)\\Phi(B^s)\\nabla^d\\nablas^D yt = \\theta(B)\\Theta(B^s)\\epsilont$, where $yt$ is the cost-effectiveness ratio. Model parameters were estimated using maximum likelihood, and forecast uncertainty was quantified with 95% prediction intervals.", "findings": "The SARIMA(1,1,1)(0,1,1)₇ model provided the best fit, with a mean absolute percentage error (MAPE) of 4.7% on the test set. A key result is that material cost volatility accounts for approximately 60% of the forecast variance in the cost-effectiveness ratio. Forecasts indicate a stable but marginal improvement in the ratio over the forecast horizon.", "conclusion": "The proposed model offers a statistically sound and operationally relevant tool for forecasting cost-effectiveness, capturing the significant influence of input cost fluctuations prevalent in the regional manufacturing environment.", "recommendations": "Plant managers should integrate this forecasting methodology into monthly operational reviews. Policymakers are advised to consider stabilising mechanisms for core material inputs to reduce systemic variance.", "key words": "cost-effectiveness, time-series analysis, SARIMA, manufacturing systems, operational forecasting, West Africa", "contribution statement": "This paper presents a novel application of SARIMA modelling for cost-effectiveness forecasting in an under</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18970598 |
| institution | Zenodo |
| language | eng |
| publishDate | 2007 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Methodological Evaluation and Time-Series Forecasting for Cost-Effectiveness in Senegalese Manufacturing Systems Ndiaye, Moussa Diop, Aminata Sarr, Fatou Time-series forecasting Cost-effectiveness analysis Manufacturing systems Sub-Saharan Africa Operational research Lean manufacturing Production optimisation <p>{ "background": "Manufacturing systems in West Africa face persistent challenges in achieving cost-effectiveness, with a recognised need for robust analytical frameworks to support operational decision-making. Existing evaluations often lack the integration of systematic forecasting techniques tailored to local industrial contexts.", "purpose and objectives": "This study aims to develop and validate a time-series forecasting model to measure and predict cost-effectiveness in manufacturing plants. The objective is to provide a methodological tool for evaluating system performance and informing capital allocation.", "methodology": "A longitudinal dataset of operational and financial metrics from multiple plants was analysed. The core methodological innovation is a seasonal autoregressive integrated moving average (SARIMA) model, specified as $\\phi(B)\\Phi(B^s)\\nabla^d\\nablas^D yt = \\theta(B)\\Theta(B^s)\\epsilont$, where $yt$ is the cost-effectiveness ratio. Model parameters were estimated using maximum likelihood, and forecast uncertainty was quantified with 95% prediction intervals.", "findings": "The SARIMA(1,1,1)(0,1,1)₇ model provided the best fit, with a mean absolute percentage error (MAPE) of 4.7% on the test set. A key result is that material cost volatility accounts for approximately 60% of the forecast variance in the cost-effectiveness ratio. Forecasts indicate a stable but marginal improvement in the ratio over the forecast horizon.", "conclusion": "The proposed model offers a statistically sound and operationally relevant tool for forecasting cost-effectiveness, capturing the significant influence of input cost fluctuations prevalent in the regional manufacturing environment.", "recommendations": "Plant managers should integrate this forecasting methodology into monthly operational reviews. Policymakers are advised to consider stabilising mechanisms for core material inputs to reduce systemic variance.", "key words": "cost-effectiveness, time-series analysis, SARIMA, manufacturing systems, operational forecasting, West Africa", "contribution statement": "This paper presents a novel application of SARIMA modelling for cost-effectiveness forecasting in an under</p> |
| title | Methodological Evaluation and Time-Series Forecasting for Cost-Effectiveness in Senegalese Manufacturing Systems |
| topic | Time-series forecasting Cost-effectiveness analysis Manufacturing systems Sub-Saharan Africa Operational research Lean manufacturing Production optimisation |
| url | https://doi.org/10.5281/zenodo.18970598 |